人工智能增强的肺听力用于心力衰竭检测
概括
人工智能 (AI) 和肺部听觉显示出在家中早期发现心力衰竭 (HF) 的前景. 这种由人工智能驱动的方法提供了一种负担得起和高效的查方法,有可能改善患者的治疗结果.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 尽管心血管疾病 (CVD) 死亡率下降,但心力衰竭 (HF) 的全球发病率正在增加.
- 传统的HF早期检测方法,如心电图 (ECG) 不适合家庭监测.
- 需要可访问和实用的家用查工具,用于HF.
研究的目的:
- 研究在家庭环境中使用人工智能 (AI) 和肺部听觉来早期检测心力衰竭的可行性.
- 开发和评估深度学习 (DL) 模型,用于分析肺部声音以识别HF.
- 探索人工智能驱动的耳语镜在非临床听觉方面的潜力.
主要方法:
- 从15名心力衰竭患者和15名健康人群中记录了肺部声音.
- 在记录的数据上训练了两个深度学习模型,一个卷积神经网络 (CNN) 和一个变压器.
- 使用独立于主体的培训和测试来评估模型的通用性.
主要成果:
- 最优的DL模型实现了71.4%的特异性和83.3%的HF检测灵敏度.
- 高频患者在750-1800赫兹频率范围内表现出更响亮的肺声音.
- 信号处理揭示了与心力衰竭相关的肺部声音的独特模式.
结论:
- 通过人工智能增强的肺部听觉,提供了一种可行的,负担得起的,快速的方法,用于在家进行HF查.
- 这项研究表明,开发人工智能耳语镜有潜力,用于早期检测心力衰竭.
- 结合信号处理和机器学习,可以克服主体依赖的变化,并提供有关高频肺部声音的新见解.
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